An intracortical brain-computer interface (iBCI) is used to record electrical signals directly from a person's brain, predict their intention from those signals, then control an assistive device (e.g., a computer cursor, prosthetic limb, or powered wheelchair) according to those intentions. This technology enables severely paralyzed people to interact with the world. However, designing robust algorithms to extract intent from recordings of single neurons is extremely challenging, in large part because of the very limited access to humans, or even monkeys, from whom these invasive recordings can be made. In this project, we will develop a model iBCI system that generates real-time biomimetic neural data by capturing the high-degree-of-freedom finger movements of able-bodied human subjects. To accomplish this, we will construct a modular recurrent neural network (RNN). The RNN will be trained to predict the motor cortex activity of a monkey from the monkey's own finger kinematics. Small modules of the RNN will be interchanged according the particular animal or recording session to model the high inter-session variability present in motor cortex. Once the modular RNN is trained, its weights will be fixed and human finger kinematics will be used as the RNN inputs, which will generate subject-controlled emulated neural activity. The emulated neural activity can be passed to iBCI decoding algorithms that control computer cursors or other physical devices, allowing human subjects to interact directly with decoders in real time, closed-loop conditions. We call this model system the jaBCI. The jaBCI is low cost and noninvasive, making it possible to rapidly test and design novel iBCI decoders using statistically rigorous sample sizes. The project will be executed in close collaboration with intracortical microelectrode array data expert Dr. Lee Miller at Northwestern University. Dr. Miller's lab, with the help of our consultant Dr. Mathis, will obtain simultaneous finger kinematics and neural activity of monkey subjects that will serve as the training data for the RNN component of the iBCI model. We will validate the emulated neural data generated by the jaBCI across many measures to ensure the model captures as many features of intracortical data as possible. These include comparing the model and actual iBCI in subject performance, learning rates, control strategies, neural variation across days, neural firing rate distributions, and low-dimensional neural dynamics. With the validated model, we will undertake a study to rigorously evaluate the highest performing, current state-of-the-art iBCI decoders. This will yield useful insight into the features of decoders that yield the greatest performance gains, overcoming the current impossibility to compare iBCI decoders in well-controlled studies using more than two or three nave human subjects. We will also use the iBCI model to evaluate novel decoder designs, and to determine the features of neural dynamics that are consistent across common iBCI tasks to help focus decoder development on those features.